Method and system for three-dimensional compression of digital video signals

ABSTRACT

Methods for compressing digital video signals that fully utilize temporal compression techniques. Each of the methods disclosed compresses digital video signals not only in the spatial domain, as with current implemented MPEG compression methods, but also in the temporal domain. A group of video signals is input to a signal compressor (16). The signal compressor (16) performs discrete cosine transforms in both the spatial and temporal domain. The transformed data is then input into a three-dimensional quantization matrix (18), where rate and distortion optimization parameters are calculated for compression purposes. In a first method, rate-distortion performance and transmission order are optimized for the quantized, three-dimensional transform coefficients. In a second method, rate-distortion performance is optimized for the quantized, three-dimensional transform coefficients. Temporal dequantization and inverse transform are performed before transmitting the two-dimensional transform coefficients in MPEG-compatible intraframe format. The data is transmitted to a signal decompressor (40).

This application is a continuation of U.S. patent application Ser. No.08/621,855, filed Mar. 25, 1996.

BACKGROUND OF THE INVENTION

1. Technical Field

This invention relates generally to digital video signal compression,and more particularly to methods of three-dimensional digital videosignal compression that exhibit a high compression ratio, minimal signaldistortion and a low transmission rate.

2. Discussion

Present video applications require large amounts of data to betransmitted at high bit rates and with a minimal amount of signaldistortion. For example, the uncompressed data bit rates for monochromedigital video, such as VCR-grade video (SIF), broadcast television video(CCIR-601) and high definition television (HDTV) are 16 Mbps, 67 Mbps,and 240 Mbps, respectively. In an uncompressed state, these data bitrates are too high to allow such video signals to be transmitted andprocessed in a commercially feasible manner. Therefore, in order toprocess such video signals in a practical manner, such video signalsmust be compressed prior to being transmitted.

In response to the proliferation of video based applications andproducts, an industry wide need for creation of a standard video signalcompression syntax arose. A group under the International StandardsOrganization (ISO), known informally as the Moving Pictures ExpertsGroup (MPEG), was formed to define standards for digital video and audiocompression. Subsequently, the MPEG has created a standardized syntax bydefining the content of a compressed video signal bit stream and themethod of decompressing the bit stream subsequent to its transmission.The methods of compression, however, have not been defined, thusallowing individual manufacturers to develop various methods of actuallycompressing the data bit stream within the defined standards.

MPEG has to date defined two syntaxes widely used in the digital videosindustry. A syntax known as MPEG-1 was defined to be applicable to awide range of bit rates and sample rates. Particularly, MPEG-1 issuitable for use in CD/ROM applications and other non-interlaced videoapplications having transmission rates of about 1.5 Mb/s. A secondsyntax known as MPEG-2 was defined for representation of broadcastvideo, and other video signal applications having coded bit rates ofbetween 4 and 9 Mb/s. MPEG-2 syntax is also applicable to applicationssuch as HDTV and other applications requiring efficient coding ofinterlaced video.

While the above discussed MPEG-1 and MPEG-2 syntaxes exhibit adequateperformance characteristics, the ongoing evolution of digital videodictates the need for further advancement in the art, as the presentMPEG video syntax definitions do have associated limitations. Forexample, temporal redundancy, a phenomenon which can be used to enhancevideo compression by minimizing data bit rate transmission fortemporarily non-changing video pixels, is an efficient method ofmaximizing video signal compression. Present MPEG-1 and 2 datacompression-based methods utilize temporal compression. However, theMPEG-1 and 2-based temporal compression is based on a frame by framejudgement basis so that the methods do not take full advantage oftemporal compression. In particular, commercially standard MPEG-1 andMPEG-2 syntaxes partially only utilize temporal redundancy. In addition,present MPEG syntax requires numerous optimization options (such aspredictive frame, bi-linear frame and intraframe variables) to becalculated, transmitted and then decoded by MPEG signal decompressors.The use of these numerous variables adds both computational time andcomplexity to the data compression. Also, while many current MPEGimplemented syntaxes exhibit an associated bit rate compression of ashigh as 30:1, increasingly complex and data-intensive video applicationsrequire higher compression rates for real-time processing. Although datacompression methods claiming compression ratios as high as 200:1 doexist, such methods subsample an array of pixels in a video framesequence (i.e., throw away every other pixel) and utilize othershortcuts to achieve high compression.

With the ever increasing need to achieve higher bit rate transmission,there is a need for a video signal data compression method that exhibitsa lower transmission rate than present MPEG-1 or MPEG-2 standards byachieving a higher compression ratio through more complete utilizationof temporal redundancy than is presently utilized by MPEG 1 and 2-basedstandards. At the same time there is a need for a data compressionmethod that is less computationally complex than current methods andthat is compatible with currently implemented video systems conformingto MPEG-1 and MPEG-2 standards.

SUMMARY OF THE INVENTION

In accordance with the teachings of the present invention, two methodsof compressing a digital video signal are provided. Both methods exhibithigher compression ratios than current MPEG standards, while at the sametime are less computationally intensive. In particular, the video signalcompression methods of the present invention utilize a temporaldimension quantization vector in addition to the standard MPEGtwo-dimensional spatial quantization matrix to optimize the compressionrate with an associated signal distortion that is unobservable to thehuman eye. Both methods may be adapted for use in an MPEG compatibleformat, thereby allowing the methods to be used with current digitalvideo signal applications.

In the inventive approach, a first method is provided for compressing adigital video signal. The method includes the step of providing acompressor for performing video signal data compression operations. Avideo signal is received at the compressor and includes spatial andtemporal data from a plurality of video signal frames. The compressorconditions the spatial and temporal data for data quantization and thenquantizes the conditioned data such that the transformed spatial data isassociated with the transformed temporal data. An optimal transmissionrate and signal distortion level is then determined from the quantizeddata. The transformed spatial data is subsequently disassociated fromits ordering to the transformed temporal data before the mix oftransformed spatial and temporal data is formatted into a matrix in azig-zag configuration block for data transmission.

A second method of data compression is also provided. The second methodprovides a signal compressor for performing video compressionoperations. Video signal data including a plurality of video frames isinput into the signal compressor. The compressor performsthree-dimensional transformation of the video signal data from spatialand temporal position domains to a three-dimensional frequency domain.The compressor then creates a plurality of three-dimensionalquantization matrices, with each matrix containing quantizationcoefficients corresponding to transform data from one temporal frequencyin the video signal. Each of the plurality of quantization matricesincludes a third dimension in which, for each temporal frequency, atemporal quantization vector component is associated with each of thequantization coefficients. The compressor then calculates maximumtransmission rate and optimal signal distortion parameters for the videoframe from the data in the quantization matrices. The optimallyquantized data is dequantized in the temporal dimension and inversetemporally transformed (one-dimensional transform). The resultingspatial transform is zig-zag ordered and transmitted per MPEGconvention.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent uponreading the following detailed description and upon reference to thedrawings, in which:

FIGS. 1a and 1b are block diagrams for the signal compressor anddecompressor, respectively, in which the signal compression methods ofthe present invention are implemented;

FIG. 2 illustrates a group of video frames in sequence containing datato be compressed by the data compression methods of the presentinvention;

FIG. 3 illustrates a sequence of two-dimensional transform framessubsequent to a discrete cosine transform being performed on the datacontained in the video frames of FIG. 2;

FIG. 4 illustrates a three-dimensional quantization matrix containingthree-dimensional coefficients subsequent to a one-dimensional discretecosine transform being performed on the data contained in the videoframes of FIG. 2;

FIG. 5 is a flow diagram illustrating the preferred method ofimplementation of a first compression method of the present invention;

FIG. 6 is a flow diagram illustrating the preferred method ofimplementation of a second data compression method of the presentinvention;

FIG. 7 is a flow diagram illustrating a preferred method of calculatingthe optimal transmission rate for use with the data compression methodsof the present invention;

FIG. 8 is a flow diagram illustrating a preferred method of calculatingthe optimal distortion rate for use with the data compression methods ofthe present invention; and

FIG. 9 is a graphical analysis of data transmission rate versus temporalfrequency quantization vector weighting factor.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring generally to FIGS. 1a-1b, a system in which the preferredembodiment of the present invention is implemented as shown generally at10a and 10b. In this system, one of the high compression data methodsaccording to the present invention may be implemented. These datacompression methods utilize temporal compression implemented through useof a three-dimensional quantization matrix. The methods of the presentinvention add a third temporal dimension to the two-dimensional spatialquantization matrix. A temporal quantization vector multiplies thecoefficients in the MPEG two-dimensional spatial matrix representing aframe of data. By adding a temporal domain dimension to the MPEG spatialcoefficient matrix, the variables associated with rate/distortionoptimization calculations in previously mentioned MPEG-based datacompression methods, such as predictive, bi-linear, and intraframevariables, are eliminated, as the temporal quantization value is theonly variable necessary for calculating rate/distortion optimizationparameters in the compression method of the present invention. As aresult, compression method and system complexity is greatly reduced andthe data compression ratio is greatly increased when compared to presentMPEG-based data compression methods.

Referring in particular to FIG. 1a, the video signal is generated at avideo signal source 12. It is contemplated that this video signal sourcecould be any source of a digital video signal, or an analog video signalthat may be later converted to digital through means such as ananalog-to-digital video frame grabber 14. Such a video signal source mayinclude, but is not limited to, a video cassette recorder (VCR), a CDROM, broadcast television signal generator, a high definition televisionsignal generator, or any type of computer network application or otherreal-time digital video source. The digital video signal generated fromthe video signal source 12 and/or the video frame grabber 14 is theninput into a signal compressor, indicated generally at 16. The signalcompressor includes a processor 18, a memory 20, and a power source 22.Also, an external storage medium, such as a central hard drive 24, maybe operatively connected to the CPU to add further computation andmemory capabilities to the compressor.

Preferably, the above described signal compressor is implemented via aSun Sparc 10 Workstation. However, it should be appreciated that othercomputers, such as an IBM or IBM compatible personal computer having aIntel Pentium® processor chip or any other processing unit havingequivalent processing capabilities can also be utilized to implement thedata compression methods of the present invention. The methods of thepresent invention may be implemented using ANSI C programming language.However, it should also be appreciated that any other computer languagecapable of implementing the present methods may be used.

In addition to the processor 18, the signal compressor also includes anapplication specific integrated circuit (ASIC) 30 for performingdiscrete cosine transforms (DCT) in accordance with the preferredmethods of the present invention as will be described below.Additionally, the signal compressor 18 also includes a histogram chip 32for forming histograms for data transmission purposes, as will also bedescribed in detail below.

Referring to FIG. 1b, a signal receiver is shown generally at 10b. Thesignal receiver includes a signal decompressor, shown generally at 40.As with the signal compressor, the decompressor may be a Sun Sparc 10Workstation, an IBM personal computer with an Intel Pentium® processor,or any other processing unit having similar processing capabilities. Itshould be appreciated that the compressor and the decompressor may infact be the same processing unit. The decompressor includes a processor42 for performing inverse transforms to reconstruct the compressed videoframes transmitted from the signal compressor 16. Specifically, an ASIC44 is implemented to perform these inverse transforms. The processor 42also includes a memory 46 and a power supply 48, of the type well knownin the art. A video monitor 50 is also connected to the processor 42 fordisplaying the transmitted, reconstructed video frames. The videomonitor is of the type well known in the art and may be a television, acomputer display, or any other commercially available pixel-based videoscreen.

Referring to FIG. 2, a sequential group of video frames generated by thevideo signal source 12 is shown generally at 52. Each of the videoframes represents a temporally successive arrangement of pixels, asindicated by the time designations T=0, T=1, etc. at 54 and as is wellknown in the art. In accordance with the methods of the presentinvention, pixel data contained in each video frame is transformed,through a two-dimensional discrete cosine transform (DCT) function bythe ASIC from a spatial position domain to a spatial frequency domain.

Additionally, a one-dimensional DCT is performed by the ASIC 30 on thetwo-dimensionally transformed spatial data to effectively produce a"temporal frequency tag" for each of the spatial transform coefficients,thereby resulting in the coefficient transforms of the video frame datashown generally in FIG. 3 at 56 and labeled w=1, w=2, etc. at 57, with wdesignating a discrete temporal frequency domain increment. Thecoefficients are quantized as described below. The discrete cosinetransform function is desirable for implementation in the methods of thepresent invention in that it closely approximates the ideal transformknown as the Karhunen Loeve transform, which is known in the signalprocessing art as the ideal transform for a random process with knowncorrelation coefficients between the terms in the random process,thereby calculating answers in a mathematical optimization manner. Thetwo-dimensional spatial DCT of the plurality of video frames is indexedby the "time tag" index t. ##EQU1## where N is the number of pixels onone side of a pixel block, and: ##EQU2## and ##EQU3##

As shown in FIG. 4, after the one and two-dimensional transforms areperformed by the ASIC 30, the resulting data for each video frame isquantized in a three-dimensional quantization matrix, shown generally at58. Each of the resulting matrices includes eight coefficients per row,as indicated by the arrow U at 59 in FIG. 4, eight coefficients percolumn, as indicated by the arrow V at 60 and temporal quantizationvectors associated with each spatial coefficient in the directionrepresented by arrow W at 61. The three-dimensional quantization matrixis formed by multiplying the 64 C_(uv) quantization coefficients fromthe MPEG two-dimensional spatial matrix by a temporal scaling termrepresented by the temporal vector elements and derived from theone-dimensional discrete cosine transform. The formula is represented asfollows:

    Q.sub.3D =q.sub.w Q.sub.MPEG

where q_(w) =(q_(w0), q_(w1), . . . , q_(w7)) is the temporal scalingterm derived from the rate-distortion optimization methods describedbelow. Thus, each matrix representing a particular video frame in theeight frame sequence includes sixty-four three-dimensional coefficients,each presented generally by the term C_(uvw) as shown generally in thematrix at 58. For the two-dimensional DCT, the DC value is the 0,0 termin the spatial domain and represents the average of intensities ofpixels in the video frame in question. The remaining 63 coefficients, orAC values, are related to the difference between pixels in temporallysuccessive frames.

Referring to FIG. 5, a preferred method of implementing a first datacompression method according to a first embodiment of the presentinvention is shown generally at 62 and will now be described. As shownat 63, the original sequence of video frames is input into thecompressor 16. The sequence of frames is then subjected to motioncompensation analysis at step 64, as is done with conventional MPEGsyntax and is as well known in the art. The general mathematicalrepresentation of the motion compensation is as follows. Thedisplacements Δx_(n) and Δy_(n) for motion compensation of a macroblock(16-by-16 pixels) in frame n (s_(n)) with respect to a macroblock fromframe 0 (S₀) are: ##EQU4## over all possible Δx_(n), Δy_(n) within awindow of pixels in frame S₀.

At step 66, a two-dimensional discrete cosine transform (DCT) asdescribed above is performed on the data contained within the frames totransform the data from the spatial position domain to the spatialfrequency domain. At step 68, a one-dimensional discrete cosinetransform is performed on the data in the video frames as describedabove in the temporal dimension to thereby produce coefficients in thetemporal frequency domain. The three-dimensional discrete cosinetransform group of transform coefficients are ordered as a group ofeight by eight matrices, with the number of matrices equaling the numberof frames used in the group of frames being processed.

Subsequent to the above discrete cosine transformations in the spatialand temporal domains being performed on the data, the processor at step70 adaptively creates a three-dimensional quantization matrix for eachgroup of frames of video data in the memory 20 by determining thetemporal quantization vector component that optimizes the rate at eachtemporal frequency.

Also at step 70, the method calculates the optimal distortion, or error,for the particular sequence of video frames being transmitted. Inaddition, at step 70, the optimal transmission bit rate is alsocalculated, as will be described in detail below. Subsequent to theoptimal distortion and bit rate transmission calculations, at step 72the method dequantizes the coefficients in the matrices formed andcreated at step 70 by removing the temporal scaling factor c_(w) fromthe coefficients. At step 74, the method performs an inverseone-dimensional DCT to remove the coefficients from the temporal domain,thereby reverting the matrix back to a two-dimensional MPEG matrix withcoefficients representing the spatial position of signal data. Thecompression method according to the present invention dequantizes thethree-dimensional matrix and performs the inverse one-dimensional DCTfunction on the coefficients prior to transmission to both takeadvantage of the temporal compression of the three-dimensional DCT andto transmit the data in such a manner that the signal decompressorreceives the intraframe coded frames in MPEG compatible form.

At step 76, the processor determines the probability of each coefficientbeing zero for the particular processed group of frames and arranges thecoefficients accordingly in a zig-zag transmission block order utilizedin conventional MPEG data compression methods. Subsequent to thecoefficients being arranged in the above-described zig-zag ordertransmission block, the data is transmitted at step 78 to the signaldecompressor 42. Subsequent to being transmitted, the zig-zag orderedblock of data is reformatted at step 80 by the signal decompressor backinto the two-dimensional MPEG spatial position coefficient matrix.Subsequently, at step 82, the signal decompressor processor 42 performsan inverse two-dimensional discrete cosine transform to revert the videodata in the spatial frequency domain C_(uvt) back to the patial positiondomain ##EQU5## where N is 8, the size of one side of the block ofpixels, and D(u) and D(v) have been previously defined. At step 83, themethod removes the motion compensation weighting from the data. At step84, the reconstructed video frames are produced and output on the videomonitor 26.

In the compression method above, it has been shown that the method maybe implemented with present MPEG compatible video systems to achieve acompression rate of about 40:1, which is a significant increase whencompared to the typical 30:1 compression rate of present MPEG-2 syntax.The above described three-dimensional DCT based method temporallycompresses four to eight frames of video and thus has more compressionpotential than the commercial standard MPEG-1 and 2 syntaxes, whichtypically utilize less than eight frames for optimizing temporalredundancy and uses temporal compression for only two frames at a time.After optimizing temporal compression, the MPEG compatible algorithmuncompresses the temporal dimension before data transmission, leaving atwo dimensional data format that is compatible with MPEG. Transmissionand compression rates are thus determined after temporal uncompression,or dequantization and inverse transformation, has been performed.

It should be appreciated at this point that, in the above method, thetemporal scaling parameter can be chosen either to set all temporalfrequency distortions at the same value or to minimize distortion for agiven target rate allocated to each temporal frequency.

Referring to FIG. 6, a second method of data compression according to apreferred embodiment of the present invention is illustrated through theflow diagram shown generally at 90. This second data compression methodattacks the transmission order optimization after video framecoefficients have been transformed three-dimensionally, rather than useMPEG zig-zag order after the inverse temporal transformation implementedin the first data compression method DCT at step 64. At step 92, theoriginal video frames are input into the compressor from the videosignal source 12. The processor 18 initially performs motioncompensation the frames at step 94. At steps 96 and 98, the methodperforms two-dimensional spatial and one-dimensional temporal DCTtransforms, respectively, on the video data, as with the above describedfirst decompression method. Also, at step 100, three-dimensionalquantization matrices are adaptively created, as in the earlierdescribed first method.

However, the second method differs in that at step 101, the histogramchip 32 creates a histogram of the quantized coefficients in thethree-dimensional matrices such that the method may adaptively create athree-dimensional coefficient transmission order (i.e., athree-dimensional zig-zag order) block. Thus, the histogram chipgenerates a histogram that is used by the compressor to transform onethree-dimensional matrix into another three-dimensional matrix based onthe information the histogram collects. An ordering based on increasingspatio-temporal frequency, i.e., ##EQU6## would be an expression of theMPEG approach to transmission order. The data compression method of thepresent invention improves on the MPEG method by adapting thetransmission order to the probability C_(uvw) =o and thus optimizing thethree-dimensional zero run length for each group of frames. Thecompressed bit stream thus includes an ordering with the coefficienthaving the least probability of being zero being first transmitted, withthe coefficients having the least probability of being zero beingtransmitted in increasing probability order. In the zig-zag order, thefirst row of the first matrix of dequantized coefficients is the columnfrom the processed group of frames with the least number of zeros. Thesecond row of the first matrix of the dequantized coefficients is thecolumn from the processed group of frames with the second least numberof zeros. The remaining rows of the first matrix are filled out withsimilar ordering regarding number of zero coefficients in a column. Rowsof the second matrix are filled if there are any remaining columns withnon-zero coefficients. An end of block character is transmitted afterthe last row in the new matrix system having a non-zero coefficient istransmitted. This minimizes the amount of data transmitted having noenergy and being represented by coefficients having a zero value. Thedecompressor at the other end subsequently fills in zeros to the rest ofthe transmitted bit stream before the decompressor starts datadecompression.

The MPEG-like ordering with increasing spatio-temporal frequency is lostupon data transmission due to the three-dimensional zig-zag block beingtransmitted, as the block must be reordered before the inverseone-dimensional transform may be performed. However, by truncating thenumber of zeros being transmitted in the high compression method andtransmitting the data in a three-dimensional block, the zero run lengthis increased in two different directions, thereby giving about afivefold increase in compression over the MPEG compatible method.

Subsequent to the three-dimensional zig-zag order block beingimplemented, the matrix coefficients are transmitted at step 102. Aftertransmitting the signal data, the method undoes the three-dimensionalzig-zag transmission order block at step 104 before the inverseone-dimensional and inverse two-dimensional DCT transforms can beperformed at steps 106 and 108, respectively. Subsequently, motioncompensation is removed, and the video frames are reconstructed andoutput to the video monitor 26 at steps 110 and 112.

It should be appreciated from the foregoing that, in general,utilization of the temporal dimension quantization matrix in both theabove described methods allows the compression ratio to be optimized,while at the same time maintains the amount of signal distortion at alevel that is not observable to the human eye. Signal compression isthus maximized while computational complexity is minimized.

Referring now to FIG. 7, a flow diagram illustrating the transmissionrate calculations performed by the above data compression methods areillustrated generally at 120. The transmission rate is calculated usingentropy calculations based on pseudo-probability distribution ofcoefficients, and not on the actual MPEG syntax calculations. Theentropy calculations slightly overestimate the transmission rate. Theentropy calculation accounts for all quantized coefficients, whereas theMPEG method truncates coefficients if the coefficients exceed certainminimum or maximum limits defined by the MPEG standard. The equation forrate determination using Huffman coding is: ##EQU7## where p is theprobability vector for non-zero transform coefficients, L_(i) is thezero run length plus one and p_(i) is the probability of a sum of izeros. MPEG software, which is publicly available on the Internet, couldbe used instead of the entropy calculations.

As shown in step 122, video signal data is input. This data correlatesto the three-dimensional quantization coefficients that fill thethree-dimensional quantization matrices performed at steps 60 and 98,respectively, of the above described first and second methods. At step124, the quantizer is stepped up gradually in small increments. At eachtemporal frequency, a temporal quantizer scaling term is determined fromoptimizing rate-distortion performance at that temporal frequency. Atstep 126, the distortion rate R is calculated. The optimal R iscalculated by plotting transmission rate versus temporal quantizationcomponent q_(w). Such a plot results in the generation of a curve with aminimum rate value, as indicated generally at 127 in FIG. 9. At step128, the processor determines if the minimum value for R has been foundby successively choosing points on the generated curve 127. At step 130,the method determines if the processor has found the minimum value of R.If the processor has found the minimum value, the value q_(w) is set tominimize the value R (q_(w)). R is an approximation to the average ratefor the group of frames, which is usually the rate for the first frameof a group of frames. Use of one frame to represent the rate for a groupof frames is typically sufficient for rate optimization. If the minimumvalue is not found, the method returns to step 128 to continue searchingfor the minimum value of R. Subsequent to step 130, the methodincrements to step 132, where steps 124-132 are repeated for q_(w1),q_(w2), . . . until the overall transmission rate for each R (q_(w)) hasbeen computed for q_(w7). At step 134, the method determines if the nextvalue for q_(w) equals q_(w8). If so, the method ends until the nextgroup of frames is compressed. If q_(w8) has not been computed, themethod returns to step 124 for further transmission rate computations.

Similarly, as shown in FIG. 8, the preferred method of implementing asignal distortion optimization calculation is shown at 150. At step 152,the scaling factor q_(w) is incremented. The distortion rate D iscalculated in parallel to the transmission rate calculation at theprocessor 18, as is indicated at step 154. The distortion rate iscalculated as: ##EQU8## As shown, the distortion rate is the square ofthe difference between the coefficients with and without temporalquantization vector weighing factor. The sum of each of the individualerror contributions for a particular coefficient gives the totaldistortion error due to quantization. R is an estimate to the truecompressed rate; it is a performance metric used to set the terms in thetemporal quantization vector during rate optimization procedure. R iscalculated in the following way. A set of quantized coefficients at aq_(w) value is made, and the three-dimensional quantized coefficientsare temporally dequantized and inverse one-dimensional DCT transformed.The rate for a frame is then determined and set equal to R. This processrepeats until the minimum R is set for each temporal frequency.

At step 156, the method determines if D (q_(w)) is above the thresholdof the human visual system. If the distortion is above the threshold,the method returns to step 152 and the distortion is calculated untilthe value of D (q_(w)) is above the threshold of human visualization.When the value is such, the q_(w) iteration is stopped as indicated atstep 158.

At step 160, the distortion rate for the next spatial transmissionmatrix for the next video frame is calculated. At step 162 the methoddetermines if D (q_(w)) has been computed for all values of w from w=0to w=7 assuming that a group consisting of eight video frames is beingcompressed. If the method is finished, the method ends until the datafrom the next group of frames is entered for processing. If the methodis not finished, the method returns to step 152 to continue calculatingthe distortion rate for the next q_(w).

It should be appreciated at this point that it has been observed thattypically only q_(w0) and q_(w1) actually have a measurable effect onthe distortion rate and bit rate transmission calculations for themethods of the present invention. Thus, the values for q_(w2) throughq_(w7) could be set at default values to further simplify theabove-described data compression methods. It should also be appreciatedthat the rate/distortion sequence of the compression methods of thepresent invention could be enhanced by optimizing both the DC and ACcoefficients separately. The rate/distortion optimization method couldalso be enhanced by using the Lagrange multiplier method.

From the foregoing explanation, it should be appreciated that the videosignal compression methods of the present invention provide methods forcompressing digital video signals in a manner that achieves high bitrate transmission with minimal detected distortion. The methods of thepresent invention maximize use of temporal compression through temporalredundancy and thereby minimize the number of quantization variablesrequired for data compression and thus the complexity of the datacompression operation. The data compression methods of the presentinvention exhibit increased compression ratios when compared to presentMPEG-1 and MPEG-2 based data compression methods while at the same timeminimize data compression complexity.

Various other advantages of the present invention will become apparentto those skilled in the art after having the benefit of studying theforegoing text and drawings, taken in conjunction with the followingsclaims.

What is claimed is:
 1. A method of compressing video signal data,comprising:providing a signal compressor for performing video signaldata compression operations; receiving a video signal at said signalcompressor, said video signal including spatial and temporal data from aplurality of video signal frames; conditioning said spatial and temporaldata for data quantization, including performing a one-dimensionaldiscrete cosine transform of said temporal data from a temporal locationdomain to a spatial frequency domain; quantizing said conditionedspatial and temporal data such that said conditioned spatial data isassociated with said conditioned temporal data; determining an optimumtransmission rate and signal distortion level from said conditionedquantized spatial and temporal data; dequantizing said conditionedquantized spatial and temporal data to disassociate said conditionedtemporal data from said conditioned spatial data; performing an inverseone dimensional discrete cosine transform of said temporal data from thespatial frequency domain to the temporal location domain; the steps ofperforming a one-dimensional discrete cosine transform of said temporaldata, quantizing said conditioned spatial and temporal data,dequantizing said conditioned quantized spatial and temporal data andperforming an inverse one dimensional discrete cosine transform of saidtemporal data, being effective to compensate for motion in said videosignal prior to data transmission, thereby minimizing data beingtransmitted; and formatting said conditioned spatial data into a matrixin a zig-zag configuration block for data transmission.
 2. The method ofclaim 1, wherein said step of conditioning said spatial and temporaldata comprises the steps of:transforming said spatial data from aspatial location domain to a spatial frequency domain; and transformingsaid temporal data from a temporal location domain to a temporalfrequency domain to maximize video signal transmission rate and tominimize video signal distortion.
 3. The method of claim 1, furthercomprising the steps of:transmitting said zig-zag configuration block toa signal decompressor; and reconstructing said video signal data fromsaid transmitted zig-zag configuration block.
 4. The method of claim 3,wherein said step of reconstructing said video signal data comprises thesteps of:undoing said zig-zag configuration block of said conditionedspatial data; and performing an inverse transform on said conditionedspatial data to reconstruct said video signal frames.
 5. The method ofclaim 1, wherein said step of conditioning said spatial data comprisesperforming a two-dimensional discrete cosine transform of said data, andsaid step of conditioning said temporal data comprises performing aone-dimensional discrete cosine transform of said data.
 6. A system fortransmitting video signal data, comprising:a signal input for receivinga video signal containing a plurality of frames of video data; a videosignal compressor, comprising:a processor for discrete cosinetransforming two-dimensional spatial domain data to two-dimensionalfrequency domain data and for transforming one-dimensional temporaldomain data to one-dimensional frequency domain data to scale saidtransformed spatial data; said processor loading said transformedspatial and temporal data into quantization matrices in an associatedmemory and performing signal distortion and transmission rateoptimization calculations based on said data in said quantizationmatrices; said processor dequantizing said one-dimensional temporal dataand performing an inverse discrete cosine transform on saidone-dimensional temporal data subsequent to performing said signaldistortion and transmission rate optimization calculations, therebycompensating for motion in the plurality of frames of video data andthereby decreasing the amount of data necessary for transmission; saidprocessor subsequently configuring said quantized spatial data in azig-zag transmission order block before transmitting said data; atransmitter for transmitting said compressed video signal data from saidprocessor; and a signal decompressor for receiving said data transmittedfrom said processor and reconfiguring said data from said zig-zagtransmission order before performing an inverse transformation of saiddata to reconstruct said video data frames.
 7. A method of transmittinga digital video signal, comprising the steps of:transformingtwo-dimensional spatial video signal information; discrete cosinetransforming one-dimensional temporal video signal information;quantizing said spatially and temporally transformed digital videosignal information to calculate optimal transmission and signaldistortion rates; dequantizing said quantized, temporally transformeddigital signal information; inverse discrete cosine transforming saidone dimensional temporally transformed digital video signal information;entropy coding said spatially transformed, quantized digital videosignal information; transmitting said entropy coded spatiallytransformed, quantized digital video signal information; andreconstructing said original digital video signal information from saidtransmitted entropy coded spatially transformed quantized digital videosignal information; said steps of discrete cosine transformingone-dimensional temporal video signal, quantizing said spatially andtemporally transformed digital video signal information, dequantizingsaid quantized, temporally transformed digital signal information, andinverse discrete cosine transforming said one dimensional temporallytransformed digital video signal information being performed prior tosaid step of transmitting said video signal information to compensatefor motion in said video signal information in lieu of computing andtransmitting motion compensation vectors.